Analysis-of-Stock-High-Frequent-Data-with-LSTM
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Gofinge
A simply framework of researching stock data through LSTM by Tensorflow
Analysis-of-Stock-High-Frequent-Data-with-LSTM
Introduction
This project aims at predicting stock price based on high frequency stock data. There is a big difference between high frequency data and others, thus certain preprocessing methods are necessary in mining useful information. LSTM is again proved effective in this problem. As a contrast, we also tested some other classical machine learning model such as XGBoost and random forest.
Experiment
Prediction of next tick's price:
We use LSTM to predict stock price, mid-price of next tick. Random Forest and XGBoost are used to classify the following price trend.
- label: next price delta
- label: next mid price delta
Prediction of future mean price:
- label: 2.5 min mean price delta
Feature importance:
The size of circle indicates its feature importance.
- model: Random Forest, label: next price delta
- model: XGBoost, label: next price delta
Gofinge